Network-Assisted disease classification and biomarker discovery

Developing improved approaches for diagnosis, treatment, and prevention of diseases is a major goal of biomedical research. Therefore, the discovery of biomarker signatures from high-throughput “omics” data is an active research topic in the fi eld of bioinformatics and systems medicine. A major issue is the low reproducibility and the limited biological interpretability of candidate biomarker signatures identifi ed from high-throughput data. This impedes the use of discovered biomarker signatures into clinical applications. Currently, much focus is placed on developing strategies to improve reproducibility and interpretability. Researchers have fruitfully started to incorporate prior knowledge derived from pathways and molecular networks into the process of biomarker identifi cation. In this chapter, after giving a general introduction to the problem of disease classifi cation and biomarker discovery, we will review two types of networkassisted approaches: (1) approaches inferring activity scores for specifi c pathways which are subsequently used for classifi cation and (2) approaches identifying subnetworks or modules of molecular networks by differential network analysis which can serve as biomarker signatures.